Overview

Dr Ho Fai Po

Biography	Dr Ho Fai Po is currently a Lecturer in Data Analytics in the School of Computing and Creative Technologies. He holds a Bachelor’s degree in Mathematics and Economics, a Master’s degree in Mathematics, and a PhD with a focus on statistical physics. Prior to joining UWE, Dr Po worked as a Research Associate in Mathematics at Aston University in the UK. During his time there, he applied information theory techniques to develop biological computing devices using human cortical neuronal networks. His work has been at the intersection of statistical physics, Bayesian inference, and principled machine learning—delivering innovative solutions in complex data analysis and modeling. Dr Po’s research interests include statistical physics, Bayesian inference, principled machine learning applications, combinatorial optimisations, and the study of complex and disordered systems. His interdisciplinary approach continually contributes to advancing the understanding and practical applications of cutting-edge computational methods in both academic and real-world scenarios.
Research Interests	Complex Systems, Bayesian Inference, Principle Machine Learning, Statistical Physics, Combinatorial Optimization, Interdisciplinary Applications of Statistical Physics

Biography

Dr Ho Fai Po is currently a Lecturer in Data Analytics in the School of Computing and Creative Technologies. He holds a Bachelor’s degree in Mathematics and Economics, a Master’s degree in Mathematics, and a PhD with a focus on statistical physics.

Prior to joining UWE, Dr Po worked as a Research Associate in Mathematics at Aston University in the UK. During his time there, he applied information theory techniques to develop biological computing devices using human cortical neuronal networks. His work has been at the intersection of statistical physics, Bayesian inference, and principled machine learning—delivering innovative solutions in complex data analysis and modeling.

Dr Po’s research interests include statistical physics, Bayesian inference, principled machine learning applications, combinatorial optimisations, and the study of complex and disordered systems. His interdisciplinary approach continually contributes to advancing the understanding and practical applications of cutting-edge computational methods in both academic and real-world scenarios.

Research Interests

Complex Systems, Bayesian Inference, Principle Machine Learning, Statistical Physics, Combinatorial Optimization, Interdisciplinary Applications of Statistical Physics